Novel microencapsulated probiotic blend for use in metabolic syndrome: design and <i>in-vivo</i> analysis
Bibliographic record
Abstract
The increasing prevalence of the metabolic syndrome has made it a medical issue that currently affects 1 in 5 Canadians. The metabolic syndrome is defined by risk factors that predispose an individual to diabetes and cardiovascular disease. Current forms of interventions have been inadequate as substantiated by the fact that the prevalence of metabolic syndrome has not reduced over the years. The objective of this study was to investigate the therapeutic benefits of a novel microencapsulated probiotic blend in treating the metabolic syndrome. Three probiotic strains were microencapsulated into alginate-polylysine-alginate (APA) microcapsules: L. rhamnosus NCIMB 6375, L. plantarum NCIMB 8826 and L. fermentum NCIMB 5221. From the results, it was observed that the microencapsulated probiotic blend significantly reduced serum total cholesterol, LDL cholesterol and triglyceride levels (reducing from 516 mg/dL to 379 mg/dL, 314 mg/dL to 231 mg/dL and 580 mg/dL to 270 mg/dL, respectively). In addition, the administration of the microencapsulated probiotic blend was found to favourably influence the gut microbiota, decreasing Firmicutes levels and increasing Bacteroidetes levels. Overall, this work demonstrates the potential a microencapsulated probiotic blend could have in targeting multiple risk factors of the metabolic syndrome; however, greater research is still needed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".